Vol. 14(12), pp. 529-539, December, 2020 DOI: 10.5897/AJBM2020.9135 Article Number: 16A1D9965530 ISSN: 1993-8233 Copyright© 2020 Author(s) retain the copyright of this article African Journal of Business Management http://www.academicjournals.org/AJBM

Full Length Research Paper

The social side of IPOs: Twitter sentiment and investors’ attention in the IPO primary market

Gian Luca Gregori, Luca Marinelli, Camilla Mazzoli and Sabrina Severini*

Department of Management, Università Politecnica delle Marche, Piazzale Martelli n.8, 60121 Ancona, Italy.

Received 12 October, 2020; Accepted 12 November, 2020

Despite in recent years the impact of market sentiment on the performance of listing firms has gained increased attention from academics and professionals, a research question that remains uncovered deals with how the retail investor sentiment and attention might impact the IPO pricing in the primary market. This analysis proposes a stochastic frontier model approach on a sample of 412 US firms listed between 2010 and 2016. The main research questions aim at revealing the effects that the number and the sentiment of the Tweets in the 3 months prior to each IPO produce in terms of the distance between the maximum achievable price and the actual offer price of the stocks. Results show that the more favorable the sentiment, the closer the offer price is set to the maximum achievable to the benefit of the issuer; on the contrary, negative sentiments seem to play no effect on the pricing, supporting the idea that investors are net buyers of attention-grabbing news. The number of Tweets shows no effect as well. Few and good is then the desirable attention and sentiment that issuers should wish for their listing firms.

Key words: Initial public offering, sentiment, investors‟ attention, underpricing, social media.

INTRODUCTION

IPOs are naturally affected by information asymmetry scientific community may in part be because such studies problems that increase the difficulties with establishing an combine two, seemingly different, disciplines, that is, appropriate value for the new shares in an untried mass communication and finance (Kolbeins, 2010). The company (Antón et al., 2011; Xia et al., 2012). Previous mass communication literature explains how the media literature attempting to draw factors which mitigate the have been able to draw people‟s attention to the stock information asymmetry - between the issuer and the market and generate interest in stocks. By solely underwriter on one side, and between the underwriter covering a topic, the media raise that subject‟s and investors on the other side - has surprisingly devoted importance in the eyes of investors, in that the more little attention to the relationship between media attention the media pays a particular subject, the more information production and IPO valuation (Bajo and important the public believes that topic to be (McCombs Raimondo, 2017). This lack of interest among the and Reynolds, 2009). Furthermore, media can influence

*Corresponding author. E-mail: [email protected].

JEL classification: G12; G14; L82

Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution License 4.0 International License 530 Afr. J. Bus. Manage.

people‟s attitudes towards subjects by portraying them in the offer price is set to the maximum achievable to the a positive or a negative light (Blankespoor et al., 2014; benefit of the issuer; on the contrary, negative sentiments Irbo and Mohammed, 2020; Tewksbury and Scheufele, charge a discount with respect to the firm‟s fair price in 2009). Yet some recent studies (Tetlock, 2007, 2011) order to ease the completion of the offer: „few and good’ seem to suggest that media coverage may be significantly is then the desirable attention and sentiment that issuers related to asset prices even when it does not reveal hard, should wish for their listing firms in order to mitigate the breaking news, contrasting with the traditional asset- information asymmetry which naturally affects new pricing theory. listings. The contribution of this paper lies in enlarging A restrained number of studies investigate the media the debate on the effect of market sentiment in the IPO information production and its effect on the IPO pricing in pricing embedding the primary market dynamics rather terms of the attention that media chase upon investors that limiting the analysis to the secondary market pricing. and of the sentiment they can generate regarding listing Although several studies have documented a positive firms, which ultimately drives retail investors‟ demand. correlation between investors‟ sentiment and the Nevertheless, such studies mainly concentrate on the underpricing, employing different proxies to capture the secondary market effect of investors‟ attention and investors‟ attention/sentiment, none of these studies market sentiment. investigate the relationship between IPO primary market A research question that remains uncovered in the IPO pricing and market sentiment. In particular, Da et al. pricing context deals with how the retail investor (2011) used the Google Search Volume Index (SVI) as a sentiment and/or attention might impact the IPO pricing in proxy of investors‟ attention and found that such attention the primary market. Truth be told, it is easily arguable that positively relates to initial returns, as an expression of the primary market players (institutional investors) are less secondary market demand. Bajo and Raimondo (2017) influenced by the sentiment generated by the media or by enlarge the perspective to the investors‟ sentiment by social networks, as they have access to more detailed relating measures of sentiment to the IPO pricing; they information and possess a greater ability in processing maintain that a positive sentiment produced by raw information than retail investors (Bajo and Raimondo, newspaper news is positively associated with the level of 2017). Nevertheless, the way the IPO offer price is set observed underpricing, suggesting that first-day returns and adjusted in the primary market is driven by several increase in response to a more benevolent press, as an factors, including the expectations that underwriters and effect of the larger generated demand. Tsukioka et al. investors have in terms of the IPO secondary market (2018) have investigated whether pre-IPO investor success1. So far, attention and sentiment are likely to sentiment on the Yahoo finance message boards relates affect the way the offer is priced in the primary market: if to the IPO initial returns. They found that investor institutional investors anticipate an aloof investor attention and sentiment positively relate to the likelihood sentiment or feeble attention in the secondary market that IPO firms set their offer price at the filing range‟s (moderate underpricing), they would demand a higher maximum point and they are also related to higher initial compensation for re-selling shares (that is, they expect returns. the underwriter to keep the offer price below its maximum With the advent of social media, the range of action of achievable value in order to leave a deliberate pre-market the literature has been extended by covering social underpricing as a compensation). media as a source of information production in IPO This study empirically tests to what extent the social pricing: Liew and Wang (2016) published the first paper media Twitter - as sentiment and attention generator - correlating Twitter and IPOs, in which they conclude that works as a benefit or a detriment for issuing firms. there is a positive correlation between investors‟ Researchers hence propose a stochastic frontier model sentiment in the days before the IPO and the IPO return where the effects of market sentiment on the primary on the first day of trading. Based on this evidence, this market pricing are revealed. Based on a sample of 412 study empirically demonstrates that when a positive US firms listed between 2010 and 2016, this analysis sentiment is disclosed by social media on the listing firms investigate the effects that market attention and the offer price is set closer to the maximum achievable to sentiment - disclosed by the number and sentiment of the the benefit of the issuer, thus supporting the role of social Tweets in the 3 months prior to each IPO - produce in networks in mitigating the information asymmetry which terms of deliberate pre-market underpricing (that is the naturally affects new listings. distance between the maximum achievable price and the actual offer price of the stocks). This study provides evidence that the more positive the sentiment, the closer HYPOTHESIS AND RELATED RESEARCH

In recent years, the impact of attention and sentiment on the performance of listing firms has gained more and 1 Hanley (1993) first documented that the adjustment of the offer price to the more attention from both academics and professionals. midpoint of the filing range is positively correlated with the underpricing, meaning that the relation of the final offer price to the offer range disclosed in One stream of literature focuses on how investors‟ the preliminary prospectus is a good predictor of the level of initial returns. sentiment might explain the IPOs pricing anomalies. As Gregori et al. 531

an example, according to Ljungqvist et al. (2006) issuers distance between the offer price and the maximum know that the presence of a class of irrationally exuberant achievable value. investors, sentiment investors will bid up prices in the after-market, leading to higher offer prices. In addition to Previous literature has demonstrated that while a positive this, Derrien (2005) maintains that regular investors sentiment is likely to increase the investors‟ interest purchase the over-valued IPO shares with the expectation towards the IPO and in turn the demand for shares, of re-sell them to sentiment investors at even higher negative sentiment appears not to be associated with the prices, the demand for shares increases, leading to price behavior on the first day of trading (Barber et al., underpricing and negative performance in the long term. 2008; Bajo and Raimondo, 2017). In particular, the study Fluctuations in investor sentiment, could also provide an of Ahmad et al. (2016) finds that media-expressed explanation of why so many companies go public during negative tone impacts on firm-level returns occasionally, some periods (hot markets) and the dramatic fall in the because only sometimes media comment contains value- number of IPOs since 2000 (Lowry, 2003). At last, relevant information or news while other times can Cornelli et al. (2006) found that over-optimism by small be sentiment (or noise). Moreover, investors are net investors can cause IPOs to trade at prices on the first buyers of attention-grabbing news: they purchase stocks day at 40.5% higher, on average, than they would have in that have caught their attention, but they are less the absence of sentiment demand. sensitive to negative information because they tend to Another steam of IPO literature has documented that only sell stocks they already own. Following these media-provided information might facilitate or inhibit the evidences, this study then tests the following hypotheses: process of investors‟ impression formation (Bhattacharya et al., 2009; Bushee et al., 2010; Pollock and Rindova, H2: a negative sentiment on the listing firm has no effect 2003). Numerous researchers have also suggested that on the distance between the offer price and the maximum the media plays an important role in legitimation achievable value. processes (Kosicki, 1993; McCombs et al., 1997; Rogers et al., 1993) especially during the waiting period when Consistent with previous literature on the attention- retail investors‟ purchases are attention-driven rather grabbing news researchers also check for the effect of than information based (Bushee et al., 2010; Bhardwaj the number of Tweets on the primary market pricing; the and Imam, 2019). However, these studies share one study here hypothesizes that a larger number of Tweets common gap in that they only consider the role of is likely to catch investors‟ attention thus possibly attention and sentiment on the secondary market by anticipating a large demand on the market, as follows: focusing on the IPO performance as measured by underpricing (Da et al., 2011; Liu et al., 2014). Despite H3: the higher the number of Tweets (investors’ attention) social media are more likely to reflect retail rather than on the listing firm, the lower the distance between the institutional attention and sentiment, previous studies offer price and the maximum achievable value. have documented that the way the offer price is set and adjusted in the primary market - where underwriters and institutional investors build the book - is largely influenced DATA by the expectations that they both have regarding the secondary market demand (Ibbotson et al., 1988; Hanley, Sample selection 1993; Thornton et al., 2009). Jiang and Li (2013), focusing on the Hong Kong market, provide evidence that The sample of US IPOs was collected from the Thomson individual investor sentiment could influence both the pre- One Deals database (TOD). Researchers searched for all market and aftermarket IPO pricing. Moreover, the study the IPOs occurring from January 2010 to December of Chung et al. (2017) shows a spillover effect from pre- 2016, on the NASDAQ and NYSE. IPOs with the market to aftermarket sentiment given that initial returns following characteristics are excluded: offer price below are significantly and positively related to the individual $5,2 non-common shares, closed-end funds, filings by investors‟ sentiment. Researchers therefore argue that a foreign-domiciled firms, Master Limited Partnerships positive sentiment, as revealed by the tone of the Tweets, (MLPs), American Depository Receipts (ADRs) and Real communicates an expected investors‟ interest towards Estate Investment Trusts (REITs). Information regarding the offer and in turn the demand for shares. If it is so, the financial statements of issuing firms from Compustat are offer price will be set as close as possible to the also included. Jay Ritter's website was also used to maximum achievable and no „discounts‟ must be obtain information regarding the market conditions and imposed to complete the offer, thus providing a favorable the rankings on US underwriters‟ reputations. pricing for the issuer in the primary market. Based on this assumption this study tests the following hypothesis: 2Stocks with a price below $5.00 per share are subject to the provisions of the

Securities enforcement Remedies and Penny Stock Reform Act of 1990, aimed H1: a positive sentiment on the listing firm reduces the at reducing fraud and abuse in the penny stock market (Ritter, 1991). 532 Afr. J. Bus. Manage.

Stock Tweets collection term with a half-normal distribution, truncated at zero.3 In other terms, for a given IPO, a point on the frontier represents the Stock Tweets represents a restricted category of Tweets; unobserved “fair” offer price, that is, the maximum price that investors are willing to pay given a set of “pricing factors” included for this reason, they have peculiarities that deserve to be in the vector of input X. The stochastic frontier assumes that a discussed, especially in terms of how they can be maximum price exists, and that actual prices fall below the collected for research purposes. According to Bar-Haim maximum for some systematic reasons such as “economic et al. (2011) stock Tweets differ from common Tweets by inefficiency” (or “deliberate premarket factors”). This deviation from having one or more references to stock symbols - as for the maximum price can be measured by a one-sided error term. As pointed out in Hunt-McCool et al. (1996), the advantage of using example the tickers preceded by the dollar sign - but also this method in IPO pricing is to avoid using aftermarket information hashtags, the labels represented by the symbol “#” that to compute IPO prices in the primary market. users apply to certain content. Stock Tweets, as well as Reber and Vencappa (2016) provided an additional contribution the common ones, are typically characterized by an by modelling the exogenous factors that influence the gap from the informal language, slang expressions or abbreviated and frontier. In other terms, when fitting the IPO offer price frontier, they ungrammatical utterances (Bar-Haim et al., 2011). Due to also explicitly model the heteroscedasticity of the one-sided error term (Kumbhakar and Lovel, 2003). Empirically, the one-sided error this low level of lexicon standardization, the automatic variance is modelled together with the frontier as: sentiment analysis could produce a relative low level of accuracy (Borromeo and Toyama, 2015). Based on this premise, in this paper the Twitter data collection and sentiment evaluation were both conducted manually by the authors. In particular, data were where gives the dimension of the deliberate premarket collected by adopting the advanced search features underpricing and Z is a vector that includes a set of variables capturing the information asymmetry such as: the market conditions provided by the Twitter platform. To detect stock Tweets, at the time of the IPO, the deal characteristics, the presence of researchers set a search criterion that has been applied third-party certification and, more generally, the uncertainty to each company. By setting the advanced search surrounding the IPO. The model presented in this study, that takes feature, this research was able to identify all the stock a leaf from Reber and Vencappa (2016), is built around a sentiment Tweets related to each company that have been variable which account for the number and nature of the Tweets published in the three months prior to the firm‟s IPO. that have interested each IPO in the sample on the 3 months prior to the listing as predictors of the distance of the price set from the Researchers then selected the content of those stock frontier. In details, researchers expect to observe deviations Tweets specifically related to the company, that is, only between the actual and optimal price correlated to the nature and those Tweets that included one ticker (Bar-Haim et al., intensity of the market sentiment as revealed by the social media. 2011). A total number of 9,560 stock Tweets were then selected and analysed. Data and measurement of variables

The model uses the offer price per share as the dependent METHODS variable. Explanatory variables are classified into two categories: “pricing factors” and “deliberate premarket factors”. As for the first This study employed a stochastic frontier approach to test all the category, following Hunt-McCool et al. (1996) and Chen et al. cross-sectional relationships stated in the hypotheses. The (2002), researchers controlled for firm size, using the logarithm of Stochastic Frontier Analysis (SFA) combines an ordinary linear the book value of the asset in the accounting period before the offer regression model with a composite error term (Aigner et al., 1977). (ASSET). To account for the riskiness of the firm the logarithm of The error term can be broken down into a symmetric error term, long-term debt (LTB) in the accounting period before the IPO was which represents the usual stochastic error terms, and an computed (Habib and Ljungqvist, 2001). As in Peng and Wang asymmetric error component. This non-idiosyncratic disturbance (2007), researchers expected a negative correlation between debt represents a systematically negative bias due to some inefficient level and the IPO market price4. To consider the potential role of pars. Widely used in estimation of production efficiency, this asymmetric information, researchers added an INDUSTRY dummy methodology has been adopted also in pricing IPOs (Hunt-McCool to account for the fact that firm's value is unlikely to be uniformly et al., 1996). Under the IPO pricing scenario, the SFA allows an distributed across the industry (Ritter, 1991). In line with previous estimation of the maximum or “efficient” offer price that would studies, researchers allocated IPO firms into 12 two-digit SIC prevail in a situation of full information, given the firm's industry sectors. The presence of different sectors allowed us to characteristics. take into consideration not only differences in riskiness but also in growth opportunities. Table 1 provides a detailed review of all the variables (pricing factors and deliberate pre-market pricing factors)

that were used in this the study along with the data sources.

3 To account for technical inefficiency, ui can be assumed to follow either half normal, truncated normal, exponential, or two-parameter gamma and represents the independently distributed non-negative random variable. Typically, in the IPO pricing context, Y is the observed offer price of 4Researchers initially put a set of firm’s characteristics variables into the model the issuer i; X is a vector of the observed firm's characteristics; β is (such as EBIT, Capital Expenditure, Research and Development expenses and a vector of parameters to be estimated; vi is the symmetric error so on). Due to the high correlation between those variables they were dropped component with a normal distribution and ui is the asymmetric error from the model to avoid multicollinearity issues. Gregori et al. 533

Table 1. Pricing factors and deliberate pre-market pricing factors in the SFA

Variable Source Description of variable Dependent variable OFFER PRICE Thomson Offer price per share in U.S.$

Panel A: Pricing factors ASSET Compustat Total assets in the accounting period before the IPO LTD Compustat Long-term debt scaled by total assets in the accounting period before the IPO INDUSTRY Compustat Industry sector classification at the two-digit SIC level

Panel B: Deliberate premarket factors SALES Thomson Total sales in the accounting period before the IPO EQ_RET Thomson Logarithm where Secondary shares retained= Share Outstanding – Total shares sold OFFER_SIZE Thomson IPO Gross proceeds scaled by total assets in the accounting period before the IPO FEE Thomson Underwriting fees in U.S.$ million UW_REP Jay Ritter Website Underwriter reputation rank VC_backed Thomson Dummy variable equal to 1 in cases where the company is backed by a venture capital company, or zero otherwise HOT_COLD Jay Ritter Website Average market UP (exclude penny stocks, units, closed-end funds, etc) in the month before the issue date INST_DEM Thomson The percentage of shares held by all institutional investors after the IPO TWEET_POS Twitter Log of total number of positive tweets regarding the IPO in the 3 months prior to the listing (excluded the first trading day) TWEET_NEG Twitter Log of total number of negative Tweets regarding the IPO in the 3 months prior to the listing (excluded the first trading day) NUM Twitter Total number of Tweets regarding the IPO in the 3 months prior to the listing (excluded the first trading day)

This table presents the definitions of the dependent and independent variables used in the SFA model. For all models, offer price per share is the dependent variable. Pricing factors and deliberate premarket factors are the independent variables. The pricing factors are derived from standard financial theory and represent the main drivers of the offer price, the primary value drivers of equity. The deliberate premarket factors include factors which explain the distance from the maximum achievable offer price. This category involves exogenous factors that do not depend on the firm's potentiality or intrinsic characteristics but that can influence the magnitude of the deliberate premarket discount. It includes proxy variables relating to issuing firm attributes, deal (offer) characteristics, third-party certification, hot/cold market indicator, private and institutional investors firms' demand for capital. The total number of Tweets regarding the IPO in the 3 months prior to the listing as well as total number of positive and negative Tweets regarding the IPO in the 3 months prior to the listing are also included. Data sources include Thomson One Deal, Compustat, Twitter, Jay Ritter's web site [http://bear.warrington.ufl.edu/ritter/ipodata.htm].

As for the second category, that is, “deliberate premarket SALES effect because it is reasonable to expect that larger and Ritter, 2004; Lowry and Murphy, 2007).5 The logarithm factors”, these variables include factors that explain the firm size implies less uncertainty, better operation of the amount of gross proceeds was used and scaled by distance of the actual price from the maximum achievable conditions, and higher efficiency (Peng and Wang, 2007). offer price. This category involves exogenous factors that Researchers used the proportion of stocks owned by 5The impact of the variable Equity Retained on underpricing is mixed do not depend on the firm's potential performance or its insiders (EQ_RET) as a measure of the risk characteristics when used in stochastic frontier models. On the one hand, Hunt- intrinsic characteristics, but that can influence the of the IPO that are negatively related to the offer price McCool et al. (1996) report a positive relationship between equity magnitude of the deliberate premarket underpricing (Reber (Beatty and Ritter, 1986). They also argued that the larger retained and estimated offer price. On the other hand, Chen et al. and Vencappa, 2016). the equity retained, the smaller the distance from the fair (2002) and Reber and Vencappa (2016) do not find a statistically The size of the firm was controlled by means of the offer price for an IPO (Bradley and Jordan, 2002; Loughran significant relationship. 534 Afr. J. Bus. Manage.

total assets in the accounting period before the IPO to account for (Liu, 2015). The first category identifies the Tweets which denote a the offer size (OFFER_SIZE) and as a signalling variable (Reber veil of optimism or, sometimes, of pure enthusiasm on the part of and Vencappa, 2016). The logarithm of fee (FEE) as a proxy for users towards the company itself. Some of the words most used by information risk was added because underwriters ask for a higher "positive Tweeters" are love, great, jump, interesting. commission when facing more severe asymmetric information The second category includes Tweets which sometimes consist problems (Meng et al., 2016). In line with Carter and Manaster of attempts, are volunteers and not, to influence the market through (1990), researchers included the variable underwriter reputation the disclosure of negative or pessimistic news about one society. (UW_REP). Generally, low risk firms attempt to reveal their low risk Sometimes sarcasm is the way „negative Tweeters‟ use to express characteristic to the market by selecting a highly prestigious their negative view on a firm. Examples of words recurring in underwriter: the more highly ranked the underwriter is, the higher negative Tweets are: fail, terrible, waste of, down. the efficiency achieved in price setting. This means that if the firm is followed by underwriters with a good reputation, the offer price is expected to be set closer to the true value of the firm. Researchers Descriptive statistics also introduced a dummy variable (VC_backed) that is equal to 1 in cases where the company is backed by a venture capital company, Table 2 presents summary statistics for the 412 IPOs in the sample. or zero otherwise. The market condition was taken into account by The average offering price is US$14.51 per share. The average including a hot and cold market indicator (HOT_COLD). This value of total assets of the listing firms prior to the offer, as a variable represents the average market underpricing in the month measure of the level of operations, is US$1.66 billion. The offer size before the issue date (excluding penny stocks, units, closed-end variable indicates that, on average, firms have US$335 million. On funds, etc). average, underwriting fees are $1 million. The average rank of an To better understand the role of institutional investors in the underwriter is 7.8, out of a maximum attainable of 9; so, bookbuilding process, this study also controlled for the institutional researchers can conclude that, on average, only highly ranked IPO demand (INST_DEM) by using the percentage of shares held underwriters followed the issues. The shares owned by insiders‟ by all institutional investors after the IPO.6 Following the argument amount to approximatively 60%, which could be a positive signal of proposed by Benveniste and Spindt (1989) and later empirically how confident the insiders are regarding the firm‟s prospects. The tested by Hanley (1993), researchers expected high (low) demand institutional demand represents around the 70% of the shares held to reveal positive (negative) information that causes the offer price after the IPO. IPOs that are VC backed represent the 60% of the to be adjusted upward (downward). The intuition behind this sample. As far as the sentiment variables are concerned, the hypothesis is that IPOs are not fully priced by underwriters because average number of Tweets in the 3 months before the IPO is 35 of the uncertainty they face as to demand for new shares.7 (out of a maximum of 337). On average, positive Tweets tend to Therefore, to increase the probability of success and to clear the exceed negative ones. aftermarket, the investment banker sets the offer price deliberately low. Researchers would expect to find that market sentiment allows investment banks to control the demand in the primary market, EMPIRICAL RESULTS AND DISCUSSION resulting in less uncertainty and a better price accuracy process. As far as the core sentiment variables are concerned (deliberate Table 3 presents the estimates of the Stochastic Frontier pre-market pricing factors), researchers collected, for each IPO in model. The output used in the stochastic frontier model is the sample, the stock Tweets released in the 3 months before the IPO, excluding the first trading day8. This study chose such a time the natural logarithm of the offer price. The inputs or frame in order to consider the signals coming from the secondary pricing factors X and the deliberate premarket factors Z market while the book is built in the weeks prior to the listing. Both used to model the variance of the non-idiosyncratic error data collection and data coding were carried out by the authors. component are those already discussed earlier. Coding instructions as well as a standardized coding worksheet Model 1 in Table 3 reports the results of the model were jointly created and agreed by the authors in order to ensure the consistency in the coding procedure. Despite the evident used to estimate the maximum offer price achievable, advantage in term of time-saving and sample size, researchers according to the Reber and Vencappa (2016) framework. intentionally avoided any automated content analysis tool; possible It only provides the basic baseline regression without the shades in the content of the Tweets (sarcasm, emoticons and relationship variables related to deals. double meanings) would in fact have probably been misunderstood As for the control variables, in contrast with Chen et al. by any automated tool. Once the Tweets were extracted, their (2002), but in line with Hunt-McCool et al. (1996) and sentiment was assessed into „positive‟ and „negative‟‟ categories Peng and Wang (2007) researchers found a positive impact of the asset book value on the IPO offer price. 6The information regarding the participation of the institutional investors to the Contrary to Koop and Li (2001), however, the study found offer are not publicly available. Therefore, as many of previous authors did, that firms belonging to industries with great growth researchers made use of the first reported holding by investors at the end of the potential, such as electronics and communications, are offering quarter as a proxy for the participation to the IPO (Reuter, 2006; Ritter and Zhang, 2007; Field and Lowry, 2009). undervalued. 7There is an asymmetry in the bankers' expected profits as the result of the SEC Moving to the variables explaining the distance from institutional constraint which prohibits adjusting the offer price ex post to clear the maximum achievable offer price, when the the primary market and the uncertainty about the exact realization of demand characteristics of the deal are considered, researchers for the issue. 8It is important to remember that the quarterly period considered did not find that the higher the equity retained by the insiders, the include the first day of trading, so the sentiment was essentially analyzed until smaller the closer to price to its maximum achievable. the eve of the IPO: this reflects once again the purpose of our work, which is to This result suggests that underwriters might take into evaluate what happened before the listing. Considering also the actual day of account equity retention when pricing the IPO because listing, a "procedural bias" would have been committed, as it is certainly more likely that a company would be Tweeted in the D-Day of its life than in the the greater the retention, the lower the probability of previous days (or months). required aftermarket price support and, consequently, the Gregori et al. 535

Table 2. Descriptive statistics.

Variable Mean Median Std. Dev. Min. Max. OFFER PRICE (US$) 14.51 14 6.33 5 65

Panel A: pricing factors ASSET (US$ million) 1,664 90.80 15,474 0.001 272,753 LTD 0.278 0.121 0.420 0 3.974

Panel B: deliberate premarket factors lnSALES 4.162 4.300 2.261 -6.214 11.557 EQ_RET 0.564 0.197 0.483 -6.428 2.717 lnOFFER_SIZE (US$ million) 4.544 4.427 0.942 1.313 9.658 FEE (US$ million) 1.191 1.4 0.470 0 3.017 UW_REP 7.833 9.001 1.829 2.001 9.001 VC_backed 0.613 1 0.487 0 1 HOT_COLD 18.588 14.1 17.065 -3.7 112.2 INST_DEM (%) 29.213 18.807 28.379 0 134.41 NUM 35.621 39 27.188 1 337 TWEET_POS 7.247 6 7.006 0 75 TWEET_NEG 2.674 2 3.490 0 29

This table presents summary statistics of the 412 US IPO of the sample. All accounting data is measured in the 3 years prior to the offer. Offer price is the offer price per shares in US$. ASSET is the book value of assets in the accounting period before the IPO. LTD is the long-term debt in the accounting period before IPO. The study also include: the equity retained as the logarithm of (1+ (Secondary shares retained)/(Shares offered)), the underwriting fees in million U.S.$. The hot and cold markets indicator represents the average market underpricing (excluding penny stocks, units, closed-end funds, etc.) in the month before the issue date. Underwriter reputation is based on tombstone rankings used in Carter and Manaster (1990) and updated on Jay Ritter's web page. The offer size is the gross proceeds scaled by total assets in the accounting period before the IPO. INST_DEM is the percentage of shares held by all institutional investors after the IPO.

lower the variance of the inefficient error component. 1 shows a positive effect of the number of Tweets on the Also, researchers find evidence that size of the firm, as pricing of the offer, as in hypothesis 1: the higher the measured by sales, is negatively related to the distance number of positive Tweet in the 3 months prior the IPO, thus supporting the idea that larger firms are perceived the smaller the distance of the offer price to its maximum as less speculative (Hunt-McCool et al., 1996; Tinic, achievable. No significant relationship is found on the 1988); the underwriter reputation is not a critical variable negative Tweets (models 2) in line with the second in explaining the offer pricing. This last result is in line hypotheses and also with previous literature which with Reber and Vencappa (2016) who conclude that demonstrated that investors are net buyers of attention- underwriters‟ reputation does not affect the level of grabbing news: they purchase stocks that have caught deliberate premarket underpricing and suggest that it is their attention, but they are less sensitive to negative the amount of money spent on underwriting, rather than information (Barber et al., 2008; Bajo and Raimondo, the choice of a particular underwriter, which is important 2017). in the primary market pricing (Koop and Li, 2001). Finally, Contrary to the expectations (hypothesis 3), the the study found a significant influence of the market attention variable „number of Tweets‟ shows no significant conditions on pricing: specifically, researchers found that effect on the pricing. the higher the average underpricing recorded in the month before the issue, the lower the distance from the frontier. In other terms, if the market is „hot‟ there is no Conclusion need for the investment bank to apply an intentional discount to guarantee the complete subscription of the In recent years, the impact of attention and sentiment on offer. This study finally found empirical evidence that the capital markets has developed as a mainstream of percentage of shares held by all institutional investors finance (Ritter, 2003). By solely covering a topic, media after the IPO (as a proxy of the institutional investors can raise people‟s attention towards that topic (McCombs demand) makes the offer price closer to its potential. and Reynolds, 2009). Furthermore, media can influence As far as the sentiment variables are concerned, model people‟s attitudes towards subjects by portraying them in 536 Afr. J. Bus. Manage.

Table 3. Stochastic Frontier Approach estimates

Parameter 1 2 3 0.062*** 0.045** 0.065*** lnASSET 2.691 2.196 2.962

-0.010 -0.001 -0.012 lnLTD -0.656 -0.057 -0.788

0.133 0.460 0.076 Oil and Gas 0.862 1.403 0.722

-0.460** -0.487*** -0.442* Manufacturing -2.021 -2.578 -1.887

-0.081 -0.106 -0.082 Computers -1.109 -1.414 -1.114

-0.462*** -0.412*** -0.389*** Electronic equipment -4.011 -3.810 -3.725

0.043 0.036 0.063 Transportation 0.331 0.308 0.463

-0.137 -0.203 -0.143 Scientific instruments -1.204 -1.576 -1.310

-0.147 0.078 -0.252** Communication -0.955 0.464 -1.986

-0.028 -0.057 -0.023 Retail -0.193 -0.470 -0.183

-0.044 -0.061 -0.043 Health -0.402 -0.655 -0.394

2.646*** 2.750*** 2.650*** _cons 21.748 21.983 22.607

lnsig2v

-3.184*** -3.990*** -3.233*** _cons -11.914 -6.840 -9.484

lnsig2u

-0.480*** -0.341*** -0.351*** lnSALES -3.238 -3.114 -2.722

-0.418* -0.667*** -0.273 EQ_RET -1.608 -2.595 -1.259

0.000 0.000 0.000 OFFER_SIZE 0.026 -0.114 -0.033

lnFEE -0.055 -0.327 0.189 Gregori et al. 537

Table 3. cont;d

-0.152 -0.844 0.612

1.324 2.066 0.885 lnUW_REP 1.501 2.141 1.130

VC_backed 0.346 0.770 0.294 0.780 1.719 0.775

-0.079*** -0.065** -0.056** HOT_COLD -2.608 -2.517 -2.447

-0.023** -0.014 -0.023** INST_DEM -2.232 -1.311 -2.188

-0.535** - - LnTWEET_POS -2.011 - -

- 0.153 - LnTWEET_NEG - 0.568 -

- - 0.097 lnNUM - - 0.560

0.130 -3.530* -1.163 _cons 0.066 -1.755 -0.654

0.204 0.136 0.199 sigma_v 7.484 3.429 5.866

All four models use the same pricing factors to estimate the fair offer price and also the same deliberate premarket factors to explain variations from the maximum achievable offer price. Model 1 includes the number of positive Tweets in the 3 months prior to the IPO, while models 2 replace the positive Tweets with the negative ones. Model 4 replaces sentiment variables with the attention variable „number of Tweets‟. ∗∗∗, ∗∗, and ∗ denote the statistical significance at the 1, 5, and 10% level, respectively. T statistics are reported in the parentheses.

a positive or a negative light (Tewksbury and Scheufele, and/or attention might impact the IPO pricing in the 2009). According to Liu (2015) sentiment analysis, is the primary market. In this paper researchers therefore test a field of study that analyzes people‟s opinions, sentiments, stochastic frontier approach where the effects of market appraisals, attitudes, and emotions toward entities and sentiment on the primary market pricing are revealed. their attributes expressed in written text. In IPO studies, Based on a sample of 412 US firms listed between 2010 the application of sentiment from social media platforms - and 2016, researchers investigate the effects that market Twitter as an example - is a new territory worthy of a attention and sentiment - disclosed by the number and substantial amount of research efforts (Liew and Wang, sentiment of the Tweets in the 3 months prior to each 2016). Previous studies maintain that a positive retail IPO - produce in terms of deliberate pre-market investor sentiment produced by mass or social media is underpricing (that is the distance between the maximum positively associated with initial returns (Da et al., 2011; achievable price and the actual offer price of the stocks). Liew and Wang, 2016; Bajo and Raimondo, 2017; Results suggest that a positive sentiment on the listing Tsukioka et al., 2018). However, such literature only firms helps to set the offer price closer to the maximum considers the role of attention and sentiment on the achievable value, thus yielding a benefit to the issuer; on secondary market by focusing on the IPO performance the contrary, negative sentiments impose a discount on as measured by underpricing. A research question that is the firm‟s fair price in order to ease the completion of the yet uncovered deals with how the retail investor sentiment offer. 538 Afr. J. Bus. Manage.

Current results provide an analysis sentiment on IPOs, as Accounting 18(2):139-159. this study supports the role of social networks in Chung CY, Kim J, Park J (2017). Individual Investor Sentiment and IPO Stock Returns: Evidence from the Korean Stock Market. Asia‐Pacific mitigating the information asymmetry which naturally Journal of Financial Studies 46(6):876-902. affects new listings. Nevertheless, according to the Cornelli F, Goldreich D, Ljungqvist A (2006). Investor sentiment and perspective presented in this paper, firms should do their pre‐IPO markets. The Journal of Finance 61(3):1187-1216. best in order to avoid any inflation in the number of Da Z, Engelberg J, Gao P (2011). In search of attention. The Journal of Finance 66(5):1461-1499. Tweets also favoring the positive sentiment over the Derrien F (2005). IPO pricing in “hot” market conditions: Who leaves number. Few and good is indeed the desirable attention money on the table? The Journal of Finance 60(1):487-521. and sentiment that issuers should wish for their listing Field LC, Lowry M (2009). Institutional versus individual investment in firms in order to maximize the benefits coming from IPOs: The importance of firm fundamentals. Journal of Financial and Quantitative Analysis 44(3):489-516. Twitter. Habib MA, Ljungqvist AP (2001). Underpricing and entrepreneurial wealth losses in IPOs: Theory and evidence. The Review of Financial Studies 14(2):433-458. CONFLICT OF INTERESTS Hanley KW (1993). The underpricing of initial public offerings and the partial adjustment phenomenon. Journal of Financial Economics 34(2):231-250. The author has not declared any conflict of interests. Hunt-McCool J, Koh SC, Francis BB (1996). Testing for deliberate underpricing in the IPO premarket: A stochastic frontier approach. The Review of Financial Studies 9(4):1251-1269. REFERENCES Ibbotson RG, Sindelar JL, Ritter JR (1988). Initial public offerings. Journal of Applied Corporate Finance 1(2):37-45. Ahmad K, Han J, Hutson E, Kearney C, Liu S (2016). Media-expressed Irbo MM, Mohammed AA (2020). Social media, business capabilities negative tone and firm-level stock returns. Journal of Corporate and performance: A review of literature. African Journal of Business Finance 37:152-172. Management 14(9):271-277. Aigner D, Lovell CK, Schmidt P (1977). Formulation and estimation of Jiang L, Li G (2013). Investor Sentiment and IPO Pricing during Pre- stochastic frontier production function models. Journal of Market and Aftermarket Periods: Evidence from Hong Kong. Pacific- Econometrics 6(1):21-37. Basin Finance Journal 23:65-82. Antón AJM, Alonso JJM, Rodriguez GS (2011). Are IPOs (Initial Public Kolbeins G (2010). The effect of media coverage on IPOs. Research in Offering) still outperforming the market? Evidence from Spain in the Social Sciences 11:89-96. period 2000 to 2010. African Journal of Business Koop G, Li K (2001) The valuation of IPOs and SEO firms. Journal of Management 5(14):5775-5783. Empirical Finance 8(4):375-401. Bajo E, Raimondo C (2017). Media sentiment and IPO Kosicki GM (1993). Problems and opportunities in agenda-setting underpricing. Journal of Corporate Finance 46:139-153. research. Journal of Communication 43:100-127. Barber BM, Odean T, Zhu N (2008). Do retail trades move Kumbhakar SC, Lovell CK (2003). Stochastic frontier analysis. markets? The Review of Financial Studies 22(1):151-186. Cambridge university press. Bar-Haim R, Dinur E, Feldman R, Fresko M, Goldstein G (2011). Available at: https://doi.org/10.1017/CBO9781139174411 Identifying and following expert investors in stock microblogs. In Liew JKS, Wang GZ (2016). Twitter sentiment and IPO performance: a Proceedings of the conference on empirical methods in natural cross-sectional examination. The Journal of Portfolio Management language processing. pp. 1310-1319. 42(4):129-135. Beatty RP, Ritter JR (1986). Investment banking, reputation, and the Liu B (2015). Sentiment analysis: Mining opinions, sentiments and underpricing of initial public offerings. Journal of Financial Economics emotions (1st ed.). New York: Cambridge University Press. Available 15(1-2):213-232. at: https://doi.org/10.1007/978-1-4899-7687-1_907 Benveniste LM, Spindt PA (1989). How investment bankers determine Liu LX, Sherman AE, Zhang Y (2014). The long-run role of the media: the offer price and allocation of new issues. Journal of Financial Evidence from initial public offerings. Management Economics 24(2):343-361. Science 60(8):1945-1964. Bhardwaj A, Imam S (2019). The tone and readability of the media Ljungqvist A, Nanda V, Singh R (2006). Hot markets, investor during the financial crisis: Evidence from pre-IPO media sentiment, and IPO pricing. The Journal of Business 79(4):1667- coverage. International Review of Financial Analysis 63:40-48. 1702. Bhattacharya U, Galpin N, Ray R, Yu X (2009). The role of the media in Loughran T, Ritter J (2004). Why has IPO underpricing changed over the internet IPO bubble. Journal of Financial and Quantitative time? Financial Management 33(3):5-37. Analysis 44(3):657-682. Lowry M (2003). Why does IPO volume fluctuate so much? Journal of Blankespoor E, Miller GS, White HD (2014). The role of dissemination Financial Economics 67(1):3-40. in market liquidity: Evidence from firms' use of Twitter™. The Lowry M, Murphy KJ (2007). Executive stock options and IPO Accounting Review 89(1):79-112. underpricing. Journal of Financial Economics 85(1):39-65. Borromeo RM, Toyama M (2015). Automatic vs. Crowdsourced McCombs M, Llamas JP, Lopez-Escobar E, Rey F (1997). Candidate Sentiment Analysis. Proceedings of the 19th International Database images in Spanish elections: Second-level agenda-setting Engineering and Applications Symposium. pp. 90-95. Available at: effects. Journalism and Mass Communication Quarterly 74(4):703- https://doi.org/10.1145/2790755.2790761 717. Bradley DJ, Jordan BD (2002). Partial adjustment to public information McCombs M, Reynolds A (2009). How the news shapes our civic and IPO underpricing. Journal of Financial and Quantitative Analysis agenda. In Media effects. Routledge pp. 17-32. 37(4):595-616. Meng JG, Zhang W, Zou G (2016). Deliberate IPO underpricing or Bushee B, Core J, Guay W, Hamm S (2010). The role of the business market misvaluation? new evidence from China. China Accounting press as an information intermediary. Journal of Accounting and Finance Review 18(4):1-22. Research 48(1):1-19. Peng Y, Wang K (2007). IPO underpricing and flotation methods in Carter R, Manaster S (1990). Initial public offerings and underwriter Taiwan–a stochastic frontier approach. Applied Economics reputation. The Journal of Finance 45(4):1045-1067. 39(21):2785-2796. Chen A, Hung CC, Wu CS (2002). The underpricing and excess returns Pollock TG, Rindova VP (2003). Media legitimation effects in the market of initial public offerings in Taiwan based on noisy trading: a for initial public offerings. Academy of Management Journal stochastic frontier model. Review of Quantitative Finance and 46(5):631-642. Gregori et al. 539

Reber B, Vencappa D (2016). Deliberate premarket underpricing and Thornton B, Adams M, Hall G (2009). Do underwriters create value in aftermarket mispricing: New insights on IPO pricing. International the determination of the IPO final offering price? Journal of Applied Review of Financial Analysis 44:18-33. Business Research 25(6):1-12. Reuter J (2006). Are IPO allocations for sale? Evidence from mutual Tinic SM (1988). Anatomy of initial public offerings of common stock. funds. The Journal of Finance 61(5):2289-2324. The Journal of Finance 43(4):789-822. Ritter JR (1991). The long-run performance of initial public offerings. Tsukioka Y, Yanagi J, Takada T (2018). Investor sentiment extracted The Journal of Finance 46(1):3-27. from internet stock message boards and IPO puzzles. International Ritter JR (2003). Investment banking and securities issuance. In Review of Economics and Finance 56:205-217. Constantinides G, Harris M, Stulz R (eds.), Handbook of the Xia N, Junyan S, Pei G (2013). Empirical study on initial public offering Economics of Finance North-Holland, New York, NY. (IPO) underpricing and long-run performance: Evidence from Chinas Ritter JR, Zhang D (2007). Affiliated mutual funds and the allocation of A-share market. African Journal of Business Management 7(11):852- initial public offerings. Journal of Financial Economics 86(2):337-368. 861. Rogers EM, Dearing JW, Bregman D (1993). The anatomy of agenda setting research. Journal of Communication 43(2):68-84. Tetlock P (2007). Giving content to investor sentiment: the role of media in the stock market. Journal of Finance 62(3):1139-1168. Tetlock PC (2011). All the news that's fit to reprint: Do investors react to stale information?. The Review of Financial Studies 24(5):1481-1512. Tewksbury D, Scheufele DA (2009). News framing theory and research. In Media effects. pp. 33-49. Routledge.